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Prediction and calibration of black soil modeling parameters based on response surface methodology and machine learning algorithms.

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Title: Prediction and calibration of black soil modeling parameters based on response surface methodology and machine learning algorithms.
Authors: Wang, Zhipeng1 (AUTHOR) 448465250@qq.com, Ma, Feng2 (AUTHOR), Zhu, Yaonan3 (AUTHOR), Wang, Hongyan4 (AUTHOR), Zhu, Tong1 (AUTHOR) tongzhu@mail.neu.edu.cn, Wang, Youzhao1 (AUTHOR) wangyz@me.neu.edu.cn, Zhao, Chaoyue1 (AUTHOR), Yu, Jin5 (AUTHOR)
Source: Particulate Science & Technology. 2025, Vol. 43 Issue 4, p534-545. 12p.
Subjects: Black cotton soil, Rolling friction, Machine learning, Static friction, Rolling (Metalwork)
Abstract: Five machine learning algorithms Decision Tree, Random Forest, Support Vector Machine (SVM), KNN, and XG Boost were used to calibrate the discrete element contact parameters of the black soil by combining the measured data on the black soil and the simulated pile load test. Firstly, the physical parameters of the black soil and the angle of stacking were determined based on physical tests. Next, Plackett-Burman tests were carried out and the following important parameters were obtained: black soil–black soil static friction coefficient, black soil–black soil rolling friction coefficient, and black soil–stainless steel rolling friction coefficient. The simulation parameters that significantly influenced the black soil stacking angles were designed for the steepest climbing tests to optimize a range of values of the significant parameters. Machine learning was performed to determine the optimal model based on the results of the response surface index results. The results show that the decision tree model has better predictive ability and stability for the stacking angle compared to Random Forest, SVR, KNN, and XG Boost models. The best combination of parameters for the black soil-black soil static friction coefficient was 0.956, the black soil–black soil rolling friction coefficient was 0.499, and the black soil–stainless steel rolling friction coefficient was 0.221. The simulation parameters can provide a reference for optimizing the simulation parameters for the subsequent soil particles. [ABSTRACT FROM AUTHOR]
Copyright of Particulate Science & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 184628260
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  Label: Title
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  Data: Prediction and calibration of black soil modeling parameters based on response surface methodology and machine learning algorithms.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhipeng%22">Wang, Zhipeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 448465250@qq.com</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Feng%22">Ma, Feng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yaonan%22">Zhu, Yaonan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Hongyan%22">Wang, Hongyan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Tong%22">Zhu, Tong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tongzhu@mail.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Youzhao%22">Wang, Youzhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangyz@me.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Chaoyue%22">Zhao, Chaoyue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Jin%22">Yu, Jin</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Particulate+Science+%26+Technology%22">Particulate Science & Technology</searchLink>. 2025, Vol. 43 Issue 4, p534-545. 12p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Black+cotton+soil%22">Black cotton soil</searchLink><br /><searchLink fieldCode="DE" term="%22Rolling+friction%22">Rolling friction</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Static+friction%22">Static friction</searchLink><br /><searchLink fieldCode="DE" term="%22Rolling+%28Metalwork%29%22">Rolling (Metalwork)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Five machine learning algorithms Decision Tree, Random Forest, Support Vector Machine (SVM), KNN, and XG Boost were used to calibrate the discrete element contact parameters of the black soil by combining the measured data on the black soil and the simulated pile load test. Firstly, the physical parameters of the black soil and the angle of stacking were determined based on physical tests. Next, Plackett-Burman tests were carried out and the following important parameters were obtained: black soil–black soil static friction coefficient, black soil–black soil rolling friction coefficient, and black soil–stainless steel rolling friction coefficient. The simulation parameters that significantly influenced the black soil stacking angles were designed for the steepest climbing tests to optimize a range of values of the significant parameters. Machine learning was performed to determine the optimal model based on the results of the response surface index results. The results show that the decision tree model has better predictive ability and stability for the stacking angle compared to Random Forest, SVR, KNN, and XG Boost models. The best combination of parameters for the black soil-black soil static friction coefficient was 0.956, the black soil–black soil rolling friction coefficient was 0.499, and the black soil–stainless steel rolling friction coefficient was 0.221. The simulation parameters can provide a reference for optimizing the simulation parameters for the subsequent soil particles. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Particulate Science & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/02726351.2025.2476672
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 534
    Subjects:
      – SubjectFull: Black cotton soil
        Type: general
      – SubjectFull: Rolling friction
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Static friction
        Type: general
      – SubjectFull: Rolling (Metalwork)
        Type: general
    Titles:
      – TitleFull: Prediction and calibration of black soil modeling parameters based on response surface methodology and machine learning algorithms.
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            NameFull: Wang, Zhipeng
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            NameFull: Ma, Feng
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            NameFull: Zhu, Yaonan
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            NameFull: Wang, Hongyan
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            NameFull: Zhu, Tong
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            NameFull: Wang, Youzhao
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            – D: 01
              M: 05
              Text: 2025
              Type: published
              Y: 2025
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              Value: 43
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